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Data reduction and fast routing: A strategy for efficient algorithms for message-passing parallel computers

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Abstract

This paper presents several algorithms for solving problems using massively parallel SIMD hypercube and shuffle-exchange computers. The algorithms solve a wide variety of problems, but they are related because they all use a common strategy. Specifically, all of the algorithms use a divide-and-conquer approach to solve a problem withN inputs using a parallel computer withP processors. The structural properties of the problem are exploited to assure that fewer thanN data items are communicated during the division and combination steps of the divide-and-conquer algorithm. This reduction in the amount of data that must be communicated is central to the efficiency of the algorithm.

This paper addresses four problems, namely the multiple-prefix, data-dependent parallel-prefix, image-component-labeling, and closest-pair problems. The algorithms presented for the data-dependent parallel-prefix and closest-pair problems are the fastest known whenN ≥P and the algorithms for the multiple-prefix and image-component-labeling problems are the fastest known whenN is sufficiently large with respect toP.

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Authors and Affiliations

  1. Department K54/802, IBM Almaden Research Center, 650 Harry Road, 95120, San Jose, CA, USA

    Jorge L. C. Sanz & Robert Cypher

Authors
  1. Jorge L. C. Sanz
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  2. Robert Cypher
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Additional information

Communicated by Alok Aggarwal.

This work was supported in part by our NSF Graduate Fellowship.

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Sanz, J.L.C., Cypher, R. Data reduction and fast routing: A strategy for efficient algorithms for message-passing parallel computers. Algorithmica 7, 77–89 (1992). https://doi.org/10.1007/BF01758752

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  • DOI: https://doi.org/10.1007/BF01758752

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